Automation Case Studies
Real projects. Real results. Every case study below documentes an actual automation system I built — the problem, the solution, the tech stack, and the outcome.
AI Lead Qualification System
✓ Sales team hours saved: 15+ hours/week
✓ Lead qualification accuracy: 92%
The Problem
The client was receiving 80–120 inbound leads per day from multiple sources (website, Meta Ads, Upwork, referrals). Their sales team was manually reviewing each lead and deciding whether to follow up, which took up to 4 hours and caused hot leads to go cold.
The Solution
I built a Make.com automation that captures every inbound lead, sends the lead data to GPT-4 with a custom qualification prompt, receives a lead score (1–10) and reasoning, then routes hot leads (7+) directly to the right sales rep via Slack instant notification while creating the HubSpot deal automatically. Lower-score leads enter a nurture sequence.
Tech Details
Multi-source webhook aggregation → OpenAI API (GPT-4) for scoring → HubSpot Contact + Deal creation → Conditional routing in Make.com → Slack notification with deal link and AI summary.
Stripe → HubSpot CRM Automation
✓ 100% payment event coverage in CRM
✓ Onboarding sequence delay: 2 days → instant
The Problem
A SaaS company's finance and customer success teams were manually checking Stripe for payment events and then updating HubSpot contacts, creating deals, and triggering onboarding emails. This took 2–3 hours daily and frequently had 1–2 day delays before new customers received onboarding.
The Solution
I built a complete Stripe webhook processor in Make.com that handles 8 different Stripe events: payment.succeeded, subscription.created, subscription.cancelled, invoice.payment_failed, customer.updated, and more. Each event type triggers specific HubSpot actions — deal creation, property updates, workflow enrollment — and Slack alerts for the relevant team.
Tech Details
Stripe webhook endpoint → Make.com router (8 branches) → HubSpot API (contacts, deals, properties) → HubSpot workflow triggers → Slack channel notifications. Full error logging with Make.com error handlers.
AI Product Data Extraction Pipeline
✓ Data processed: 500+ items/day
✓ Extraction accuracy: 97%+
The Problem
An e-commerce business received supplier product catalogs as PDFs and unstructured emails. Employees spent 8+ hours daily manually extracting product names, SKUs, pricing, dimensions, and specs into Airtable. Errors in this process caused fulfillment issues.
The Solution
I built a pipeline that monitors a Gmail inbox for supplier emails, extracts attachments, sends document content to OpenAI with a structured extraction prompt, and automatically creates/updates Airtable records with the extracted data. Python scripts handle PDF parsing for complex catalog formats.
Tech Details
Gmail watch → Make.com → Python PDF parser (Cloud Function) → OpenAI GPT-4 structured output → Airtable upsert (with deduplication) → Slack confirmation. Full error logging and human review queue for low-confidence extractions.
WhatsApp Lead Automation System
✓ First-touch response: instant vs 6+ hours
✓ Manual follow-up eliminated entirely
The Problem
A real estate agency generated leads from Facebook and landing pages, but follow-up happened via manual WhatsApp messages sent hours later. By the time agents messaged, most leads had already been contacted by competitors.
The Solution
I built an automated WhatsApp drip sequence triggered instantly on lead capture. Using the WhatsApp Business API, leads receive a personalized first message within 30 seconds, followed by a 5-step sequence over 72 hours. Reply detection pauses the sequence and notifies the assigned agent in real-time.
Tech Details
Facebook Lead Ads → Make.com → GoHighLevel contact creation → WhatsApp Cloud API (instant message) → 5-step delayed sequence (Make.com scheduler) → Reply webhook detection → GHL conversation + agent notification.
Google Sheets AI Data Parser
✓ Processing time: 1000 rows in under 4 minutes
✓ Categorization accuracy: 94%+
The Problem
A marketing agency received weekly raw performance data in Google Sheets from clients — unformatted, inconsistent naming, missing categories. An analyst spent 3+ hours every Monday normalizing, categorizing, and reformatting data before it could be used for reporting.
The Solution
I built a Google Apps Script + Make.com solution that triggers on new spreadsheet uploads, batches rows in groups of 20, sends each batch to OpenAI for intelligent categorization and normalization, then writes clean structured data back to the target sheet automatically.
Tech Details
Google Apps Script trigger → Make.com webhook → Row batching (20 rows/request) → OpenAI structured output (JSON schema) → Google Sheets API write-back → Summary Slack notification with row count and confidence stats.
Let's Build Your Automation System
Every case study above started with a 30-minute call. Tell me what you need automated and I'll design the system.